An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus-Reference-Cited by-同舟云学术

An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus

Published:2024-07 Issue: Volume: Page:1-26
ISSN:0954-4828
Container-title:Journal of Engineering Design
language:en
Short-container-title:Journal of Engineering Design

Author:

Lyu Pin¹,Yue Yongyong¹,Yu Wengbing¹,Xiao Liqiao²,Liu Chao³,Zheng Pai²

Affiliation:

1. School of Electronic Information Engineering, Shanghai Dianji University, Shanghai, People’s Republic of China

2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

3. College of Engineering and Physical Sciences, Aston University, Birminham, UK

Funder

Shanghai Science and Technology Program

Mainland-Hong Kong Joint Funding

Innovation and Technology Commission

Hong Kong Special Administration Region

National Key R&D Programs of Cooperation on Science and Technology Innovation

Ministry of Science and Technology of China

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09544828.2024.2340392

Reference43 articles.

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3. Exploring topic modelling for generalising design requirements in complex design

4. Devlin, J., M. Chang, K. Lee, and K. Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceeding Conference North American Chapter Association Computational Linguistics: Human Language Technolgies, 4171–4186.

5. Ghaddar, A., and P. Langlais. 2018. “Robust Lexical Features for Improved Neural Network Named-Entity Recognition.” In Proceeding 27th International Conference Computional Linguistics, 1896–1907.